Energy harvesting is a promising alternative to batteries for autonomous WSN nodes. Although there are various energy harvesting methods using ambient energy sources, this research is focused on thermal energy harvesting because there are abundant thermal sources in industrial sites. As only several mW can be obtained by a thermal energy harvester based on a thermoelectric generator (TEG), improvement of the energy conversion efficiency of the thermal energy harvester is essential. This paper proposes a novel maximum power point tracking (MPPT) method combining coarse adjustment and fine adjustment to enhance the energy conversion efficiency of the harvester. The Direct Adjustment (DA) method for MPPT is adopted in the coarse adjustment stage to avoid disconnecting TEG modules from conversion circuits, while an observation and self-optimization (O&SO) method for MPPT is used in the fine adjustment stage to avoid introducing perturbations and to maintain a stable output power at the maximum power point (MPP). After building up the system simulation model, a series of experiments are conducted to evaluate the proposed MPPT methods. The results show that the DA+O&SO method is an effective MPPT method for a WSN node powered by a TEG. It is able to enhance the regulation speed of the MPPT, improve the continuity of P out , and avoid the power oscillations around the MPP.

Generally, WSNs or IoT nodes are powered by energy-constrained batteries, which significantly limit their operating lifetime and application. Harvesting energy from the surrounding environment provides a promising solution for self-powered WSNs or IoT nodes. Compared with other energy harvesting approaches, thermoelectric energy harvesting based on thermoelectric generators (TEGs) has many advantages. However, the power output of TEGs is difficult to be maintained at its maximum power point (MPP) due to the fluctuation of ambient temperature. To achieve the maximum power point tracking (MPPT) based-on internal resistance matching method for self-powered WSNs or IoT nodes using TEG, this paper proposes a simple approach to obtain the models of TEG open-circuit voltage, Seebeck coefficient, and internal resistance. The proposed method is verified by a series of experiments on a commercial TEG module. The results indicate that the presented models are more accurate and simple than the existing models reported by other authors.